LLM test
What is an LLM test?
An LLM test is typically a repeatable prompt (or prompt set) used to:
- Measure output quality
- Catch regressions
- Test for specific risks (e.g., hallucinations, formatting, refusals)
- Validate model alignment with task instructions or user expectations
LLM tests can be standalone or part of a broader GenAI testing framework.
Why LLM testing matters
Without structured testing, LLMs may:
- Produce unpredictable outputs across prompts or contexts
- Fail to meet quality or tone requirements
- Regress in performance during updates
Running systematic tests helps ensure LLMs are production-ready.
Types of LLM tests
1. Prompt-based tests
- Fixed input prompts used to evaluate consistency, accuracy, and safety
- Track how output changes over time or across versions
2. Scenario-based tests
- Multi-turn or contextual tests simulating user interactions
- Helps evaluate agent behavior, memory, or chain-of-thought reasoning
3. Rubric-driven evaluations
- Tests scored using defined criteria (e.g., clarity, helpfulness, tone)
- Can be scored manually or with LLM-as-a-Judge
4. Behavioral and stress tests
- Evaluate how the model handles edge cases, adversarial inputs, or conflicting instructions
5. Regression tests
- Compare new outputs against baselines from previous versions
Related
Designing a robust LLM test suite is key to continuous evaluation and quality assurance in generative AI applications.